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02/08/2026 11:52 am
TL;DR: Most breaches are still discovered by outsiders, while median attacker dwell time rose to 11 days in 2024 and automated detection misses living-off-the-land abuse, according to Mandiant, SANS, Verizon and IBM. AI-augmented hunting matters because it expands analyst capacity, not because it replaces judgment.
NHIMG editorial — based on content published by Dropzone AI: Proactive Threat Hunting: Why It's Critical and How AI Makes It Scalable
By the numbers:
- 57% of all compromises were discovered by external parties, not the organization's own security team.
- 76% of organizations have encountered living-off-the-land techniques from nation-state actors.
- The median breach lifecycle from identification through containment stretches to 241 days.
Questions worth separating out
Q: How should security teams implement proactive threat hunting in hybrid environments?
A: Start with a small set of hypotheses tied to real attacker behavior, then search across identity, endpoint, cloud, and network telemetry for evidence that normal alerts would miss.
Q: Why do living-off-the-land attacks create so many blind spots for defenders?
A: Because they use legitimate administrative tools and approved processes, so signature-based controls often see ordinary activity instead of malicious intent.
Q: What do security teams get wrong about dwell time and compromise detection?
A: They often treat dwell time as a reporting metric rather than a control gap.
Practitioner guidance
- Map hunt hypotheses to identity abuse paths Build hunt queries around credential harvesting, valid-account misuse, privileged session anomalies, and lateral movement through service accounts.
- Correlate endpoint, cloud, and identity telemetry Join SIEM, EDR, cloud logs, and IAM signals into a shared investigation path so LOTL activity is visible as a sequence rather than isolated events.
- Prioritise accounts with standing privilege Review the identities most likely to enable lateral movement, especially admin accounts, service accounts, and remote access paths that can reach sensitive systems.
What's in the full article
Dropzone AI's full blog post covers the operational detail this post intentionally leaves for the source:
- The full workflow for turning hypotheses into hunt queries across SIEM, EDR, and cloud telemetry
- The article's AI Threat Hunter model for separating analyst judgment from automated search and correlation
- Examples of how continuous hunting can replace ad hoc manual hunts without removing human decision-making
- The vendor's explanation of how its own SOC-oriented workflow is structured in practice
👉 Read Dropzone AI's analysis of proactive threat hunting and AI-assisted detection →
Proactive threat hunting in SOCs - are your controls keeping up?
Explore further
02/08/2026 1:01 pm
Proactive hunting is becoming a governance issue, not just a SOC technique. The article shows that detection stacks can be present while compromise still goes unseen, which means visibility is a programme-level control problem. For identity teams, that creates a direct link between hunting and the ability to spot suspicious credential use across human, service, and workload identities. Practitioners should treat hunting coverage as part of identity assurance, not an optional SOC enhancement.
A question worth separating out:
Q: How should security teams use AI to speed up threat hunting without losing analyst judgment?
A: Use AI to gather evidence, link related entities, and suggest likely next questions, but keep the analyst in control of the final decision. The right model accelerates investigation work, not judgement. Require traceable sources, visible queries, and a clear path from clue to conclusion so the hunt remains reviewable and defensible.
👉 Read our full editorial: Proactive threat hunting is the gap reactive security cannot close
